In a radio communications system, channel capacity grows with the number of antennas. To obtain larger system capacity than that of a single-antenna system, a multi-input-multi-output (MIMO) transmission system has multiple antennas placed respectively at a transmitter and a receiver so as to increase spectrum efficiency of radio links and improve reliability of the links Channels of a MIMO system are called MIMO channels. For more efficient use of the MIMO channels, researchers propose various methods to obtain MIMO channel capacity, such as space time coding, and precoding.
Space multiplexing and diversity are often adopted in MIMO techniques. MIMO space multiplexing implements precoding by fully using channel state information so as to improve system performance. MIMO precoding preprocesses a transmitted signal by using channel state information at the transmitter. Such techniques are closed loop MIMO techniques, including space time coding with precoding, multiplexing, and joint transmit and receive technology. The MIMO broadcast downlink scenario commonly seen in practice is especially widely studied.
In the MIMO broadcast downlink scenario, Dirty Paper Coding is an optimized solution which can obtain the maximum sum of capacity. However, Dirty Paper Coding is yet only a result of information theory. The optimal Dirty Paper Coding is still an outstanding issue with no practical system application. Therefore, most research concentrates on precoding that is easier to implement.
Multi-user precoding can be realized in different methods and can be categorized to linear precoding and nonlinear precoding.
Linear precoding includes interference elimination, such as zero-forcing and block diagonalization. The interference cancellation method further includes zero-forcing and block diagonalization. Zero-forcing means precoding is adopted at the transmitter so as to eliminate interference of other non-expected signals at every receiver.
In addition to multi-user precoding, Gaussian channel capacity in a radio communications system is also a hot topic of research. Gaussian interference channel capacity is an issue not yet effectively solved. Gaussian interference channel means Signals of different users interfere with each other, data cannot be shared among users and joint transmission of data is impossible even if every user knows the complete channel information.
Because the mutual interference cannot be eliminated from the interference channel, there are two traditional solutions: one is to process interference as noises where interference is weak; the other is orthogonalization which, however, offers small capacity, only 1/K log(SNR)+o(log(SNR)).
In recent years, the Gaussian interference channel has been studied continuously and it is found that the single-user capacity border of a symmetrical Gaussian interference channel is ½ log(SNR)+o(log(SNR)). It is also found that the capacity limit can be approached by interference alignment.
Interference alignment separates the useful signal from the interference signal in terms of space at a receiver by means of preprocessing at a transmitter when the complete channel information is known, while interference of different transmitters on the receiver is aligned into one spatial dimension, thus avoiding the impact of the interference and increasing capacity.
Nevertheless, in the conventional art, after the transmitter preprocesses a signal, the transmitted signal is distributed in the real part and the imaginary part and then sent to the receiver. The useful part in the receiver may be distributed in the real part and imaginary part of the receive signal, or the entire space of the received signal. To suppress interference, it is necessary to align the interference of both the real part and imaginary part with zero so that the space of the control vector of the useful signal is constrained and that the Signal to Noise Ratio (SNR) is hard to improve. Therefore, system performance is limited in the conventional art.